Blockchain technology is increasingly recognized as a transformative tool for improving agri-food supply chain (ASC) performance, particularly in enhancing transparency and traceability. Nonetheless, it has still not been widely adopted in developing nations like Pakistan. This study investigates the impact of blockchain adoption on ASC performance, incorporating transparency as a mediating variable and the Internet of Things (IoT) as a moderating factor within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Data were collected from 333 professionals in Pakistan's agri-food sector and analyzed using smart PLS, with mediation and moderation effects. The findings reveal that blockchain adoption significantly improves supply chain performance. Transparency partially mediates this relationship, while IoT strengthens the effect by enabling real-time data sharing and monitoring. The study offers new insights into literature by extending UTAUT through the integration of blockchain and IoT, and by empirically validating their combined impact in a developing economy context. In practice, the results provide practical information to policy makers and industry players with a view to improving efficiency, accountability, and sustainability in the agri-food supply chains.

This study examines how circular value is created under conditions of structural fragility, disruption, and resource scarcity, where prevailing circular supply chain theories offer limited explanatory power. While prior research emphasizes efficiency and optimization, it provides limited insight into how circular supply chains function when stable infrastructure, predictable flows, and formal coordination mechanisms are absent. Drawing on dynamic capabilities theory and insights from humanitarian logistics, this study proposes a conceptual framework that reconceptualizes circular value creation as an adaptive and capability-mediated process. The framework posits that digital circular capabilities, sensing, seizing, and reconfiguring, enable organizations to develop adaptive reverse logistics systems, which serve as the primary mechanism through which circular value is realized under constrained conditions. Structural fragility and disruption intensity are introduced as boundary conditions shaping the effectiveness of these relationships. The study contributes by reframing circular supply chain theory beyond efficiency-based assumptions, extending dynamic capabilities to disrupted and resource-constrained environments, and positioning reverse logistics as a central adaptive mechanism for circular value recovery. By integrating insights from humanitarian logistics and circular supply chain research, the study advances a more context-sensitive understanding of supply chain functioning under disruption.

Artificial Intelligence (AI) technologies have revolutionized maritime logistics and supply chain operations as much as they have revolutionized the manner in which other basic services are provided and exported in the maritime world, especially for the export-oriented Micro, Small and Medium Enterprises (MSMEs). This study delves into how the adoption of AI affects maritime supply chain resilience for MSME exporters in the India–Thailand trade corridor. The research combines the Technology Acceptance Model (TAM) and Dynamic Capability Theory, aiming to analyse the influence of perceived usefulness, technological readiness, digital integration and organizational adaptability on AI adoption and resilience of the maritime supply chain. The research method utilized was a cross-sectional survey which uses quantitative empirical research design. The primary data obtained were 268 responses from MSME exporters of maritime trade between India and Thailand. The collected data were analysed using a software tool called a Partial Least Squares (PLS) and a Software Statistical Package (SPSS) version 29 (SmartPLS 4). The results indicate that the use of AI has a positive and significant impact on maritime supply chain resilience. Perceived usefulness and technological readiness emerged as significant factors affecting AI adoption, and digital integration and organization's adaptability as factors affecting maritime supply chain resilience. AI-powered solutions like predictive analytics, intelligent cargo tracking, route optimization, and automated logistics systems are pivotal in bolstering the operational continuity and resilience of maritime supply chains, as emphasized in the study. The research has theoretical contributions by building on TAM and Dynamic Capability Theory and applying them to the maritime logistics context and implications for MSMEs, policy makers and maritime authorities on digital transformation and the resilient maritime trade systems. The study provides a comprehensive overview of the role of AI in maritime supply chains for achieving greater competitiveness and sustainability in new global trade contexts.

This research evaluates the psychometric properties of a modified instrument designed to examine green logistics implementation, organizational outcomes, and the regulatory influence of government. Drawing from validated 5-point Likert scales in current literature, the study utilized a pilot sample of 50 participants, with data processed via SPSS v30.0. The refinement of the scale was achieved through internal consistency checks and exploratory factor analysis (EFA). Findings confirmed robust reliability, as Cronbach's alpha coefficients for all constructs surpassed the 0.7 threshold. The final 40-item scale, organized into five theoretically aligned dimensions, demonstrated strong factor loadings and sampling adequacy (KMO > 0.7). By validating these core constructs, this work establishes a rigorous foundation for future empirical inquiries into the environmental performance of the trucking industry.

Green supply chain management has emerged as a critical strategic approach for improving environmental sustainability and organizational performance in manufacturing firms. However, existing literature remains limited and missed the mechanisms and contextual conditions that shape these relationships. This study examines the relationship between green supply chain management and organizational performance through green operational capability as mediating mechanism, while considering perceived governmental environmental signaling intensity as moderated mediator. The study employed a quantitative research approach with explanatory research design. The data were collected from 327 respondents who are working in manufacturing factories. Data were analyzed using Smart PLS to test both the measurement and structural models. The findings show that green supply chain management has significant effect on organizational performance and green operational capability. Green operational capability also significantly enhances organizational performance and mediated the relationship between green supply chain management and organizational performance. Furthermore, perceived governmental environmental signaling intensity significantly strengthen the relationship between green supply chain management and green operational capability then influence the organizational performance which confirm moderated mediation effect. This study suggests that manufacturing firms institutionalize green supply chain management as a strategic capability. Policymakers should strengthen clear and consistent environmental signaling to enhance firm responsiveness. Managers should develop green operational capabilities to effectively translate sustainability practices into improved organizational performance and long-term competitive advantage.